Colour image quality assessment using Laplacian pyramid decomposition
Manisha Jadhav, Yogesh Dandawate, Narayan Pisharoty · International Journal of Computational Vision and Robotics · 2015
Today's world has witnessed tremendous increase in use of multimedia and internet. This demands an image quality metric capable of evaluating image quality, accurately and automatically. Natural scenes are of excellent quality and all natural scenes exhibit similar statistical properties. Natural scene statistics is successfully used in image quality assessment which is based on the hypothesis that introduction of distortion in an image causes deviation from statistical properties. Amount of deviation in the statistical property of an image is found to be proportional to the amount of distortion. A neural network-based image quality metric needs such natural scene statistical feature to predict the image quality blindly. This paper presents a new feature for colour image quality assessment that is extracted after decomposing given image into different frequency bands of hue plane. In future, this feature will be used in a classifier to evaluate colour image quality.